suppressPackageStartupMessages({
library(tidyverse)
library(lubridate)
library(modelr)
library(broom)
library(lmtest)
library(sandwich)
library(viridis)
})
Henter csv. filen:
pm2 <- read_csv("data/pm2.csv", show_col_types = FALSE)
New names:
* `` -> ...1
Muterer:
pm2 <- pm2 %>%
mutate(
fnr = str_sub(knr, 1,2),
aar_f = str_sub(aar)
)
head(pm2)
parse_factor funksjonen:
pm2 %>%
mutate(
fnr = parse_factor(fnr, levels = fnr),
aar_f = parse_factor(aar_f, levels = aar_f)
)
muterer:
pm2 <- pm2 %>%
mutate(
Trade_pc_100K = Trade_p/100000
)
head(pm2, n = 4)
mod1 <- 'pm2 ~ aar_f + Total_ya_p + inc_k1 + inc_k5 + uni_k_mf + uni_l_mf + Trade_pc_100K'
lm1 <- lm(mod1, data = pm2, subset = complete.cases(pm2))
summary(lm1)
Call:
lm(formula = mod1, data = pm2, subset = complete.cases(pm2))
Residuals:
Min 1Q Median 3Q Max
-8516.6 -1472.1 -29.9 1467.3 15736.3
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -20400.74 2663.02 -7.661 2.79e-14 ***
aar_f2009 104.15 244.77 0.426 0.670512
aar_f2010 908.13 245.16 3.704 0.000217 ***
aar_f2011 1663.93 245.86 6.768 1.68e-11 ***
aar_f2012 2240.48 247.10 9.067 < 2e-16 ***
aar_f2013 2869.30 248.31 11.555 < 2e-16 ***
aar_f2014 2863.22 250.54 11.428 < 2e-16 ***
aar_f2015 3525.22 253.08 13.929 < 2e-16 ***
aar_f2016 4274.99 255.81 16.711 < 2e-16 ***
aar_f2017 5146.33 258.50 19.909 < 2e-16 ***
Total_ya_p 582.44 38.94 14.957 < 2e-16 ***
inc_k1 -376.99 30.29 -12.445 < 2e-16 ***
inc_k5 194.35 22.87 8.498 < 2e-16 ***
uni_k_mf -82.02 29.42 -2.788 0.005357 **
uni_l_mf 1206.86 42.22 28.585 < 2e-16 ***
Trade_pc_100K 871.99 218.42 3.992 6.77e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2531 on 2124 degrees of freedom
Multiple R-squared: 0.8346, Adjusted R-squared: 0.8334
F-statistic: 714.3 on 15 and 2124 DF, p-value: < 2.2e-16
pm2 %>%
add_residuals(lm1)
head(pm2, n = 4)
Man leser ut gjennomsnittlig kvadratmeterpris for en enebolig (\(pm2\)) for de forskjellige årene. Vi ser at \(pm2\) stiger jevnt og trutt.
Vi vil si at fortegnene er som forventet. Dersom vi har tolket modellen riktig, så vil \(pm2\) være mindre for dem nederste kvintilen (inc_k1) enn for den øverste (inc_k5). Det samme gjelder for de med kort og lang utdanning.
Dette er nok fordi den rikere delen av befolkninge, og de med høyere utdanning, sannsynligvis har mer attraktive eneboliger som gjør at \(pm2\) er høyere.
bptest(lm1)
studentized Breusch-Pagan test
data: lm1
BP = 352.89, df = 15, p-value < 2.2e-16
Veldig høy p-verdi. Da kan \(H_0\) forkastes og vi kan med sterke bevis si at det foreligger Heteroskedastisitet.
coeftest(lm1)
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -20400.742 2663.022 -7.6607 2.790e-14 ***
aar_f2009 104.150 244.767 0.4255 0.6705118
aar_f2010 908.129 245.156 3.7043 0.0002174 ***
aar_f2011 1663.926 245.857 6.7679 1.685e-11 ***
aar_f2012 2240.475 247.095 9.0672 < 2.2e-16 ***
aar_f2013 2869.297 248.315 11.5551 < 2.2e-16 ***
aar_f2014 2863.224 250.537 11.4283 < 2.2e-16 ***
aar_f2015 3525.223 253.083 13.9291 < 2.2e-16 ***
aar_f2016 4274.990 255.812 16.7114 < 2.2e-16 ***
aar_f2017 5146.326 258.498 19.9086 < 2.2e-16 ***
Total_ya_p 582.436 38.941 14.9568 < 2.2e-16 ***
inc_k1 -376.989 30.291 -12.4455 < 2.2e-16 ***
inc_k5 194.354 22.871 8.4979 < 2.2e-16 ***
uni_k_mf -82.023 29.424 -2.7876 0.0053574 **
uni_l_mf 1206.857 42.219 28.5853 < 2.2e-16 ***
Trade_pc_100K 871.993 218.422 3.9922 6.768e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
vcovHC(lm1)
(Intercept) aar_f2009 aar_f2010 aar_f2011 aar_f2012
(Intercept) 9297989.37 -26519.17426 -34751.3931 -64358.9799 -88195.7750
aar_f2009 -26519.17 42579.51052 22306.6988 22379.0191 22461.1963
aar_f2010 -34751.39 22306.69876 41857.2132 22643.0594 22816.5776
aar_f2011 -64358.98 22379.01911 22643.0594 45210.7304 23406.9880
aar_f2012 -88195.78 22461.19628 22816.5776 23406.9880 47055.4187
aar_f2013 -93332.22 22562.49160 23016.0483 23690.1311 24270.5328
aar_f2014 -128032.51 22647.20878 23232.1454 24076.5421 24791.9383
aar_f2015 -177893.27 22637.74268 23267.9132 24237.7165 25055.0255
aar_f2016 -229170.12 22623.80635 23323.0788 24446.1520 25385.7301
aar_f2017 -231919.09 22624.44448 23352.3686 24515.4258 25408.7607
Total_ya_p -134378.95 89.41919 277.8154 681.8928 1112.5721
inc_k1 -48847.48 -46.78668 -117.7882 188.8338 193.4766
inc_k5 -26724.41 110.78484 126.8286 397.1950 455.5137
uni_k_mf -23624.40 -129.42390 -212.3787 -468.5265 -572.7298
uni_l_mf 79213.28 -45.36231 -237.3954 -324.3915 -491.9711
Trade_pc_100K 145568.84 497.16540 1261.8579 987.3383 936.1196
aar_f2013 aar_f2014 aar_f2015 aar_f2016 aar_f2017
(Intercept) -93332.21682 -128032.5143 -177893.2733 -229170.1243 -231919.0869
aar_f2009 22562.49160 22647.2088 22637.7427 22623.8064 22624.4445
aar_f2010 23016.04825 23232.1454 23267.9132 23323.0788 23352.3686
aar_f2011 23690.13111 24076.5421 24237.7165 24446.1520 24515.4258
aar_f2012 24270.53282 24791.9383 25055.0255 25385.7301 25408.7607
aar_f2013 49220.90256 25428.8815 25755.4473 26135.5595 26169.5465
aar_f2014 25428.88146 53475.4422 27156.8674 27482.0673 27045.3309
aar_f2015 25755.44730 27156.8674 63394.1122 28309.5656 27655.2812
aar_f2016 26135.55952 27482.0673 28309.5656 75087.4602 28071.1160
aar_f2017 26169.54649 27045.3309 27655.2812 28071.1160 89424.5717
Total_ya_p 1311.74280 1662.7240 2349.7551 3130.9906 3266.6554
inc_k1 -23.25608 237.9932 438.1822 706.9105 723.9683
inc_k5 419.80206 750.9501 927.6337 1166.2786 1178.1709
uni_k_mf -695.90501 -198.2867 136.4018 -110.1222 -816.2879
uni_l_mf -632.27758 -2195.0185 -3034.7846 -2540.7427 -1110.7783
Trade_pc_100K 2510.69810 2684.4013 2764.2300 282.6406 1862.4720
Total_ya_p inc_k1 inc_k5 uni_k_mf uni_l_mf
(Intercept) -134378.94615 -48847.47803 -26724.4053 -23624.40438 79213.27980
aar_f2009 89.41919 -46.78668 110.7848 -129.42390 -45.36231
aar_f2010 277.81538 -117.78822 126.8286 -212.37867 -237.39541
aar_f2011 681.89276 188.83384 397.1950 -468.52650 -324.39148
aar_f2012 1112.57212 193.47663 455.5137 -572.72977 -491.97106
aar_f2013 1311.74280 -23.25608 419.8021 -695.90501 -632.27758
aar_f2014 1662.72401 237.99318 750.9501 -198.28673 -2195.01848
aar_f2015 2349.75511 438.18220 927.6337 136.40176 -3034.78456
aar_f2016 3130.99055 706.91052 1166.2786 -110.12216 -2540.74265
aar_f2017 3266.65535 723.96826 1178.1709 -816.28793 -1110.77830
Total_ya_p 2167.75020 426.37025 133.2185 51.21924 -614.02732
inc_k1 426.37025 801.89764 496.4444 158.26504 -500.25996
inc_k5 133.21845 496.44438 547.3448 104.53767 -690.28424
uni_k_mf 51.21924 158.26504 104.5377 1515.96690 -2398.54359
uni_l_mf -614.02732 -500.25996 -690.2842 -2398.54359 5463.68941
Trade_pc_100K -1619.34164 -2293.03278 -115.1786 -2608.77275 651.94105
Trade_pc_100K
(Intercept) 145568.8365
aar_f2009 497.1654
aar_f2010 1261.8579
aar_f2011 987.3383
aar_f2012 936.1196
aar_f2013 2510.6981
aar_f2014 2684.4013
aar_f2015 2764.2300
aar_f2016 282.6406
aar_f2017 1862.4720
Total_ya_p -1619.3416
inc_k1 -2293.0328
inc_k5 -115.1786
uni_k_mf -2608.7728
uni_l_mf 651.9410
Trade_pc_100K 60897.1826
pm2 <- pm2 %>%
add_residuals(lm1)
pm2 <- pm2 %>%
mutate(aar_d = make_date(aar))
pm2 <- pm2 %>%
mutate(fylke = substr(knr, start = 1, stop = 2))
pm2 %>%
filter(fylke %in% c("01", "02", "03", "11", "12")) %>%
unnest(c(fylke)) %>%
group_by(fylke, aar_d) %>%
summarize(mean_fylke = mean(resid)
) %>%
ggplot(aes(x = aar_d, y = mean_fylke, colour = fylke)) +
geom_line(lwd=1) +
theme(legend.position = "bottom")+
geom_hline(yintercept = 0, colour = "black")
`summarise()` has grouped output by 'fylke'. You can override using the `.groups` argument.
mod2 <- 'pm2 ~ aar_f*fnr + Total_ya_p + inc_k1 + inc_k5 + uni_k_mf + uni_l_mf + Trade_pc_100K'
lm2 <- lm(mod2, data = pm2)
summary(lm2)
Call:
lm(formula = mod2, data = pm2)
Residuals:
Min 1Q Median 3Q Max
-8546 -1191 32 1198 8328
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -21200.688 2521.645 -8.407 < 2e-16 ***
aar_f2009 94.009 744.240 0.126 0.899496
aar_f2010 417.129 744.379 0.560 0.575290
aar_f2011 1280.914 744.731 1.720 0.085597 .
aar_f2012 1455.525 745.679 1.952 0.051088 .
aar_f2013 2479.533 746.367 3.322 0.000910 ***
aar_f2014 2795.831 747.254 3.741 0.000188 ***
aar_f2015 3987.973 748.109 5.331 1.09e-07 ***
aar_f2016 5264.965 749.169 7.028 2.89e-12 ***
aar_f2017 6618.572 749.430 8.831 < 2e-16 ***
fnr02 -1482.789 702.970 -2.109 0.035045 *
fnr03 3248.234 2190.443 1.483 0.138260
fnr04 -1049.219 774.264 -1.355 0.175537
fnr05 -1937.388 758.293 -2.555 0.010696 *
fnr06 -2172.731 772.094 -2.814 0.004941 **
fnr07 -737.995 1080.348 -0.683 0.494620
fnr08 -3213.279 878.620 -3.657 0.000262 ***
fnr09 -1219.813 913.691 -1.335 0.182020
fnr10 -281.375 852.265 -0.330 0.741323
fnr11 -565.360 771.927 -0.732 0.464012
fnr12 -903.071 742.464 -1.216 0.224012
fnr14 -3339.829 1182.013 -2.826 0.004768 **
fnr15 -3619.198 715.832 -5.056 4.69e-07 ***
fnr16 -1093.217 759.677 -1.439 0.150296
fnr17 -2005.965 917.216 -2.187 0.028860 *
fnr18 -1567.503 774.530 -2.024 0.043126 *
fnr19 -2856.881 1326.142 -2.154 0.031341 *
fnr20 -2656.315 1180.088 -2.251 0.024500 *
Total_ya_p 511.787 36.100 14.177 < 2e-16 ***
inc_k1 -243.050 27.007 -9.000 < 2e-16 ***
inc_k5 251.645 22.916 10.981 < 2e-16 ***
uni_k_mf 178.253 28.157 6.331 3.02e-10 ***
uni_l_mf 732.442 42.235 17.342 < 2e-16 ***
Trade_pc_100K 1067.760 190.885 5.594 2.54e-08 ***
aar_f2009:fnr02 -40.505 978.026 -0.041 0.966969
aar_f2010:fnr02 792.694 978.020 0.811 0.417747
aar_f2011:fnr02 992.480 978.070 1.015 0.310359
aar_f2012:fnr02 1565.161 978.102 1.600 0.109716
aar_f2013:fnr02 1953.373 978.298 1.997 0.045996 *
aar_f2014:fnr02 2019.269 978.649 2.063 0.039214 *
aar_f2015:fnr02 2401.120 979.036 2.453 0.014273 *
aar_f2016:fnr02 3656.344 979.067 3.735 0.000193 ***
aar_f2017:fnr02 4707.776 979.374 4.807 1.65e-06 ***
aar_f2009:fnr03 84.133 3068.211 0.027 0.978127
aar_f2010:fnr03 2004.378 3068.354 0.653 0.513677
aar_f2011:fnr03 3891.025 3068.768 1.268 0.204970
aar_f2012:fnr03 5674.403 3069.281 1.849 0.064642 .
aar_f2013:fnr03 5108.375 3070.149 1.664 0.096297 .
aar_f2014:fnr03 4938.603 3071.105 1.608 0.107979
aar_f2015:fnr03 6985.367 3073.112 2.273 0.023131 *
aar_f2016:fnr03 10264.572 3074.072 3.339 0.000856 ***
aar_f2017:fnr03 13986.613 3075.071 4.548 5.74e-06 ***
aar_f2009:fnr04 -330.219 1089.318 -0.303 0.761813
aar_f2010:fnr04 -191.813 1089.355 -0.176 0.860250
aar_f2011:fnr04 -775.700 1089.399 -0.712 0.476523
aar_f2012:fnr04 -808.528 1089.510 -0.742 0.458115
aar_f2013:fnr04 -1206.685 1089.615 -1.107 0.268240
aar_f2014:fnr04 -1456.367 1089.708 -1.336 0.181550
aar_f2015:fnr04 -1912.336 1089.754 -1.755 0.079446 .
aar_f2016:fnr04 -2459.017 1089.893 -2.256 0.024169 *
aar_f2017:fnr04 -3549.658 1089.920 -3.257 0.001146 **
aar_f2009:fnr05 416.862 1069.758 0.390 0.696816
aar_f2010:fnr05 655.342 1069.794 0.613 0.540221
aar_f2011:fnr05 183.865 1069.834 0.172 0.863563
aar_f2012:fnr05 820.104 1070.017 0.766 0.443507
aar_f2013:fnr05 -198.536 1070.094 -0.186 0.852832
aar_f2014:fnr05 -254.055 1070.253 -0.237 0.812388
aar_f2015:fnr05 -1326.089 1070.254 -1.239 0.215480
aar_f2016:fnr05 -2117.228 1070.338 -1.978 0.048059 *
aar_f2017:fnr05 -2397.820 1070.176 -2.241 0.025165 *
aar_f2009:fnr06 -163.759 1089.292 -0.150 0.880516
aar_f2010:fnr06 189.332 1089.409 0.174 0.862046
aar_f2011:fnr06 33.963 1089.394 0.031 0.975132
aar_f2012:fnr06 800.976 1089.455 0.735 0.462302
aar_f2013:fnr06 410.281 1089.375 0.377 0.706497
aar_f2014:fnr06 571.152 1089.474 0.524 0.600167
aar_f2015:fnr06 22.631 1089.626 0.021 0.983431
aar_f2016:fnr06 -598.671 1089.701 -0.549 0.582801
aar_f2017:fnr06 60.036 1089.704 0.055 0.956069
aar_f2009:fnr07 134.353 1525.051 0.088 0.929808
aar_f2010:fnr07 728.914 1525.112 0.478 0.632745
aar_f2011:fnr07 275.017 1525.266 0.180 0.856930
aar_f2012:fnr07 1047.940 1525.235 0.687 0.492122
aar_f2013:fnr07 890.998 1525.236 0.584 0.559173
aar_f2014:fnr07 582.123 1525.332 0.382 0.702772
aar_f2015:fnr07 990.944 1525.354 0.650 0.515996
aar_f2016:fnr07 447.813 1525.278 0.294 0.769099
aar_f2017:fnr07 960.018 1525.236 0.629 0.529146
aar_f2009:fnr08 329.317 1240.237 0.266 0.790631
aar_f2010:fnr08 1281.636 1240.345 1.033 0.301597
aar_f2011:fnr08 646.495 1240.336 0.521 0.602269
aar_f2012:fnr08 1090.416 1240.413 0.879 0.379470
aar_f2013:fnr08 575.599 1240.249 0.464 0.642628
aar_f2014:fnr08 689.084 1240.251 0.556 0.578548
aar_f2015:fnr08 -776.910 1240.290 -0.626 0.531130
aar_f2016:fnr08 -1716.491 1240.468 -1.384 0.166595
aar_f2017:fnr08 -2045.538 1240.415 -1.649 0.099294 .
aar_f2009:fnr09 686.715 1288.922 0.533 0.594245
aar_f2010:fnr09 986.486 1288.914 0.765 0.444149
aar_f2011:fnr09 599.582 1288.944 0.465 0.641860
aar_f2012:fnr09 1071.846 1289.011 0.832 0.405779
aar_f2013:fnr09 64.585 1289.204 0.050 0.960050
aar_f2014:fnr09 -186.541 1289.179 -0.145 0.884965
aar_f2015:fnr09 -1242.730 1289.232 -0.964 0.335201
aar_f2016:fnr09 -1987.219 1289.181 -1.541 0.123368
aar_f2017:fnr09 -3223.036 1289.344 -2.500 0.012510 *
aar_f2009:fnr10 231.288 1199.909 0.193 0.847172
aar_f2010:fnr10 924.121 1199.916 0.770 0.441302
aar_f2011:fnr10 168.648 1199.944 0.141 0.888243
aar_f2012:fnr10 321.458 1200.216 0.268 0.788856
aar_f2013:fnr10 -515.180 1200.200 -0.429 0.667793
aar_f2014:fnr10 -674.319 1200.339 -0.562 0.574335
aar_f2015:fnr10 -1492.749 1200.502 -1.243 0.213856
aar_f2016:fnr10 -3090.918 1200.777 -2.574 0.010124 *
aar_f2017:fnr10 -3807.142 1200.767 -3.171 0.001545 **
aar_f2009:fnr11 -414.412 1069.772 -0.387 0.698515
aar_f2010:fnr11 642.468 1069.866 0.601 0.548235
aar_f2011:fnr11 1243.418 1070.024 1.162 0.245359
aar_f2012:fnr11 1467.212 1070.665 1.370 0.170728
aar_f2013:fnr11 1179.371 1071.062 1.101 0.270979
aar_f2014:fnr11 -183.391 1071.523 -0.171 0.864124
aar_f2015:fnr11 -1489.385 1072.451 -1.389 0.165063
aar_f2016:fnr11 -3274.743 1072.946 -3.052 0.002303 **
aar_f2017:fnr11 -3863.610 1073.185 -3.600 0.000326 ***
aar_f2009:fnr12 21.853 1036.805 0.021 0.983186
aar_f2010:fnr12 381.898 1036.801 0.368 0.712658
aar_f2011:fnr12 165.379 1036.901 0.159 0.873297
aar_f2012:fnr12 669.171 1037.128 0.645 0.518864
aar_f2013:fnr12 -69.430 1037.183 -0.067 0.946636
aar_f2014:fnr12 -147.825 1037.277 -0.143 0.886690
aar_f2015:fnr12 -711.755 1037.476 -0.686 0.492767
aar_f2016:fnr12 -901.775 1037.688 -0.869 0.384941
aar_f2017:fnr12 -2046.447 1038.104 -1.971 0.048828 *
aar_f2009:fnr14 -220.698 1663.985 -0.133 0.894498
aar_f2010:fnr14 536.844 1663.957 0.323 0.747009
aar_f2011:fnr14 1984.847 1664.012 1.193 0.233090
aar_f2012:fnr14 1739.551 1664.177 1.045 0.296018
aar_f2013:fnr14 208.353 1664.208 0.125 0.900381
aar_f2014:fnr14 253.302 1664.812 0.152 0.879084
aar_f2015:fnr14 -1695.187 1665.139 -1.018 0.308783
aar_f2016:fnr14 -1552.417 1665.259 -0.932 0.351330
aar_f2017:fnr14 -2074.192 1665.271 -1.246 0.213077
aar_f2009:fnr15 205.720 998.429 0.206 0.836779
aar_f2010:fnr15 548.008 998.671 0.549 0.583249
aar_f2011:fnr15 463.880 998.884 0.464 0.642414
aar_f2012:fnr15 463.860 999.265 0.464 0.642556
aar_f2013:fnr15 7.994 999.213 0.008 0.993617
aar_f2014:fnr15 -481.056 999.093 -0.481 0.630220
aar_f2015:fnr15 -587.449 999.385 -0.588 0.556727
aar_f2016:fnr15 -1872.887 999.582 -1.874 0.061126 .
aar_f2017:fnr15 -2799.827 999.681 -2.801 0.005149 **
aar_f2009:fnr16 -346.631 1069.772 -0.324 0.745955
aar_f2010:fnr16 -237.962 1069.934 -0.222 0.824020
aar_f2011:fnr16 -497.945 1069.952 -0.465 0.641705
aar_f2012:fnr16 380.682 1070.437 0.356 0.722154
aar_f2013:fnr16 -347.235 1070.757 -0.324 0.745754
aar_f2014:fnr16 -229.362 1070.812 -0.214 0.830418
aar_f2015:fnr16 -139.973 1070.880 -0.131 0.896019
aar_f2016:fnr16 -1074.143 1070.970 -1.003 0.316004
aar_f2017:fnr16 -2278.453 1070.923 -2.128 0.033499 *
aar_f2009:fnr17 -288.412 1288.940 -0.224 0.822969
aar_f2010:fnr17 -422.338 1289.001 -0.328 0.743214
aar_f2011:fnr17 257.671 1289.086 0.200 0.841590
aar_f2012:fnr17 637.493 1289.624 0.494 0.621133
aar_f2013:fnr17 203.405 1289.762 0.158 0.874704
aar_f2014:fnr17 -61.073 1289.824 -0.047 0.962239
aar_f2015:fnr17 -867.834 1289.740 -0.673 0.501107
aar_f2016:fnr17 -1612.215 1290.487 -1.249 0.211703
aar_f2017:fnr17 -2761.733 1290.527 -2.140 0.032479 *
aar_f2009:fnr18 -148.285 1089.412 -0.136 0.891744
aar_f2010:fnr18 402.939 1089.510 0.370 0.711545
aar_f2011:fnr18 252.454 1089.674 0.232 0.816812
aar_f2012:fnr18 482.679 1089.761 0.443 0.657871
aar_f2013:fnr18 201.272 1090.026 0.185 0.853524
aar_f2014:fnr18 -393.115 1090.258 -0.361 0.718459
aar_f2015:fnr18 -439.127 1090.372 -0.403 0.687190
aar_f2016:fnr18 -1361.291 1090.771 -1.248 0.212178
aar_f2017:fnr18 -2661.041 1090.689 -2.440 0.014785 *
aar_f2009:fnr19 453.061 1872.733 0.242 0.808864
aar_f2010:fnr19 982.125 1872.779 0.524 0.600045
aar_f2011:fnr19 -669.729 1872.850 -0.358 0.720682
aar_f2012:fnr19 727.671 1872.902 0.389 0.697670
aar_f2013:fnr19 278.261 1873.128 0.149 0.881921
aar_f2014:fnr19 1688.165 1873.121 0.901 0.367563
aar_f2015:fnr19 369.085 1873.412 0.197 0.843839
aar_f2016:fnr19 906.286 1873.612 0.484 0.628646
aar_f2017:fnr19 -716.410 1873.886 -0.382 0.702272
aar_f2009:fnr20 -927.061 1664.164 -0.557 0.577542
aar_f2010:fnr20 -547.207 1664.063 -0.329 0.742313
aar_f2011:fnr20 -542.321 1664.293 -0.326 0.744568
aar_f2012:fnr20 -378.342 1664.741 -0.227 0.820240
aar_f2013:fnr20 -1110.163 1664.836 -0.667 0.504960
aar_f2014:fnr20 -1563.827 1665.176 -0.939 0.347778
aar_f2015:fnr20 -3266.760 1665.444 -1.961 0.049964 *
aar_f2016:fnr20 -3169.910 1665.821 -1.903 0.057200 .
aar_f2017:fnr20 -3922.387 1665.464 -2.355 0.018615 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2105 on 1944 degrees of freedom
Multiple R-squared: 0.8953, Adjusted R-squared: 0.8848
F-statistic: 85.21 on 195 and 1944 DF, p-value: < 2.2e-16
pm2 <- pm2 %>%
mutate(res_m2 = resid(lm2))
Delplott:
pm2 %>% filter(fnr %in% c("01", "02", "04", "11", "12")) %>%
ggplot(mapping = aes(x = aar_d, y = res_m2)) +
geom_line(aes(group = knavn)) +
scale_size_manual(values = c(seq(2.0, 0.5, by = -0.1))) +
geom_hline(yintercept = 0) +
theme(legend.position = 'bottom') +
facet_wrap(~fylke)
Kvaliteten på modellen er ikke helt optimal da den mangler noen variabler. Dette kan ha noe med heteroskedatisitet i modell at det er stor variasjon. Det er store residualer, spesielt i Rogaland.
Ut i fra grafene så ser man at variasjonen er stor. Dette indikerer et heteroskedastisitetsproblem, og dermed er det grunn til at det er utelatte viktige variabler (brudd på TS.3/TS’.3)
pm2 %>% filter(fnr %in% c("11")) %>%
ggplot(mapping = aes(x = aar_d, y = res_m2)) +
scale_color_viridis(discrete = TRUE, option = "D") +
geom_line(aes(group = knavn, colour = knavn, size =knavn)) +
scale_size_manual(values = c(seq(2.0, 0.5, by = -0.1))) +
geom_hline(yintercept = 0) +
theme(legend.position = 'bottom')
pm2 %>% filter(knr %in% c("1119", "1120", "1127", "1121", "1130", "1135", "1106", "1149")) %>%
ggplot(mapping = aes(x = aar_d, y = res_m2)) +
scale_color_viridis(discrete = TRUE, option = "H") +
geom_line(aes(group = knavn, colour = knavn, size =knavn)) +
scale_size_manual(values = c(seq(2.0, 0.5, by = -0.1))) +
geom_hline(yintercept = 0) +
theme(legend.position = 'bottom')
Stavanger-kommunene overvurderes (HÃ¥, Klepp og Randaberg).
pm2_n <- pm2 %>%
group_by(aar) %>%
select(pm2, fnr, knr, aar, aar_f, Menn_ya_p, Kvinner_ya_p, Total_ya_p, inc_k1, inc_k5, uni_k_mf, uni_l_mf, Trade_pc_100K) %>%
nest()
pm2_n
pm2_n$data[[1]] %>%
head(n = 5)
dim(pm2_n)
[1] 10 2
kom_model <- function(a_df) {
lm(pm2 ~ fnr + Total_ya_p + inc_k1 + inc_k5 + uni_k_mf + uni_l_mf + Trade_pc_100K, data = pm2)
}
pm2_n <- pm2_n %>%
mutate(model = map(data, .f = kom_model))
kom_model(pm2_n$aar) %>%
summary()
Call:
lm(formula = pm2 ~ fnr + Total_ya_p + inc_k1 + inc_k5 + uni_k_mf +
uni_l_mf + Trade_pc_100K, data = pm2)
Residuals:
Min 1Q Median 3Q Max
-10648.8 -1602.8 -168.1 1474.5 14320.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6480.17 2872.86 2.256 0.024194 *
fnr02 151.87 314.23 0.483 0.628913
fnr03 7275.88 959.78 7.581 5.11e-14 ***
fnr04 -2866.03 317.21 -9.035 < 2e-16 ***
fnr05 -2728.80 305.31 -8.938 < 2e-16 ***
fnr06 -2048.90 312.29 -6.561 6.70e-11 ***
fnr07 -198.84 434.89 -0.457 0.647557
fnr08 -3439.76 356.42 -9.651 < 2e-16 ***
fnr09 -2211.31 367.84 -6.012 2.16e-09 ***
fnr10 -1357.67 346.79 -3.915 9.33e-05 ***
fnr11 -354.75 345.19 -1.028 0.304213
fnr12 -1067.22 318.04 -3.356 0.000806 ***
fnr14 -3685.59 483.41 -7.624 3.68e-14 ***
fnr15 -3897.81 307.47 -12.677 < 2e-16 ***
fnr16 -2039.39 304.44 -6.699 2.69e-11 ***
fnr17 -3222.93 376.12 -8.569 < 2e-16 ***
fnr18 -2229.33 316.67 -7.040 2.59e-12 ***
fnr19 -2938.14 530.36 -5.540 3.40e-08 ***
fnr20 -4283.12 477.15 -8.976 < 2e-16 ***
Total_ya_p 136.67 42.46 3.219 0.001306 **
inc_k1 -387.33 33.05 -11.720 < 2e-16 ***
inc_k5 42.66 26.93 1.584 0.113318
uni_k_mf 278.27 34.36 8.099 9.28e-16 ***
uni_l_mf 1030.52 50.75 20.305 < 2e-16 ***
Trade_pc_100K 1075.31 238.65 4.506 6.97e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2639 on 2115 degrees of freedom
Multiple R-squared: 0.8209, Adjusted R-squared: 0.8189
F-statistic: 403.9 on 24 and 2115 DF, p-value: < 2.2e-16
pm2_n %>%
filter(aar%in% c("2008")) %>%
.$model %>%
map_df(glance) %>%
print()
mod_sum <- pm2_n %>%
filter(aar %in% c("2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017")) %>%
mutate(mod_summary = map(.x = model, .f = glance)) %>%
unnest(mod_summary) %>%
print()
coef_df <- mod_sum$model %>%
map_df(1) %>%
as.tibble()